Scene change detection in digitized video is the process of identifying changes in the video data that are likely to represent semantically meaningful changes in the video content, typically associated with a change in shot or scene. Scene change detection is useful particularly for video indexing and archiving, and allows for user to “browse” video. Conventional scene change detection is typically applied during the playback of video. Numerous algorithms have been created to identify scene changes, relying variously on histogram analysis of color, luminance, and other image characteristics, and in some cases on analysis of motion vectors. These approaches have been likewise extended to identify scene changes in compressed video content, such as MPEG-2 video. However, these approaches require the computationally expensive process of first decoding the compressed video prior to do the scene change analysis, in order to obtain the necessary statistics and data, such as motion vectors, color data, and the like.
Some researchers have explored using motion vector information created during the MPEG encoding process, relying on the amount and magnitude of motion vectors, as well as mean square prediction error, to identify scene changes relative to a last reference frame. This approach however may falsely identify scene changes where there has been gradually changing image content in an otherwise continuous scene. In addition, these methods do not advantageously adjust the bit rate used for image encoding, but instead attempt to improve image quality to forcing a change in the frame type.
Accordingly its desirable to provide an improved scene identification process during the encoding of compress video. It is further desirable to use the scene change information to modify the bit rate for improved image quality.